Latex Posters

LaTeX Research Posters

Overview

Research posters are a critical medium for scientific communication at conferences, symposia, and academic events. This skill provides comprehensive guidance for creating professional, visually appealing research posters using LaTeX packages. Generate publication-quality posters with proper layout, typography, color schemes, and visual hierarchy.

When to Use This Skill

This skill should be used when:

• Creating research posters for conferences, symposia, or poster sessions
• Designing academic posters for university events or thesis defenses
• Preparing visual summaries of research for public engagement
• Converting scientific papers into poster format
• Creating template posters for research groups or departments
• Designing posters that comply with specific conference size requirements (A0, A1, 36×48", etc.)
• Building posters with complex multi-column layouts
• Integrating figures, tables, equations, and citations in poster format

AI-Powered Visual Element Generation

STANDARD WORKFLOW: Generate ALL major visual elements using AI before creating the LaTeX poster.

This is the recommended approach for creating visually compelling posters:

1. Plan all visual elements needed (title, intro, methods, results, conclusions)
2. Generate each element using scientific-schematics or Nano Banana Pro
3. Assemble generated images in the LaTeX template
4. Add text content around the visuals

Target: 60-70% of poster area should be AI-generated visuals, 30-40% text.

---

CRITICAL: Preventing Content Overflow

⚠️ POSTERS MUST NOT HAVE TEXT OR CONTENT CUT OFF AT EDGES.

Common Overflow Problems:

1. Title/footer text extending beyond page boundaries
2. Too many sections crammed into available space
3. Figures placed too close to edges
4. Text blocks exceeding column widths

Prevention Rules:

1. Limit Content Sections (MAXIMUM 5-6 sections for A0):

✅ GOOD - 5 sections with room to breathe:
   - Title/Header
   - Introduction/Problem
   - Methods
   - Results (1-2 key findings)
   - Conclusions

❌ BAD - 8+ sections crammed together:
   - Overview, Introduction, Background, Methods, 
   - Results 1, Results 2, Discussion, Conclusions, Future Work

2. Set Safe Margins in LaTeX:

% tikzposter - add generous margins
\documentclass[25pt, a0paper, portrait, margin=25mm]{tikzposter}

% baposter - ensure content doesn't touch edges
\begin{poster}{
  columns=3,
  colspacing=2em,           % Space between columns
  headerheight=0.1\textheight,  % Smaller header
  % Leave space at bottom
}

3. Figure Sizing - Never 100% Width:

% Leave margins around figures
\includegraphics[width=0.85\linewidth]{figure.png}  % NOT 1.0\linewidth

4. Check for Overflow Before Printing:

# Compile and check PDF at 100% zoom
pdflatex poster.tex

# Look for:
# - Text cut off at any edge
# - Content touching page boundaries  
# - Overfull hbox warnings in .log file
grep -i "overfull" poster.log

5. Word Count Limits:

• A0 poster: 300-800 words MAXIMUM
• Per section: 50-100 words maximum
• If you have more content: Cut it or make a handout

---

CRITICAL: Poster-Size Font Requirements

⚠️ ALL text within AI-generated visualizations MUST be poster-readable.

When generating graphics for posters, you MUST include font size specifications in EVERY prompt. Poster graphics are viewed from 4-6 feet away, so text must be LARGE.

⚠️ COMMON PROBLEM: Content Overflow and Density

The #1 issue with AI-generated poster graphics is TOO MUCH CONTENT. This causes:

• Text overflow beyond boundaries
• Unreadable small fonts
• Cluttered, overwhelming visuals
• Poor white space usage

SOLUTION: Generate SIMPLE graphics with MINIMAL content.

MANDATORY prompt requirements for EVERY poster graphic:

POSTER FORMAT REQUIREMENTS (STRICTLY ENFORCE):
- ABSOLUTE MAXIMUM 3-4 elements per graphic (3 is ideal)
- ABSOLUTE MAXIMUM 10 words total in the entire graphic
- NO complex workflows with 5+ steps (split into 2-3 simple graphics instead)
- NO multi-level nested diagrams (flatten to single level)
- NO case studies with multiple sub-sections (one key point per case)
- ALL text GIANT BOLD (80pt+ for labels, 120pt+ for key numbers)
- High contrast ONLY (dark on white OR white on dark, NO gradients with text)
- MANDATORY 50% white space minimum (half the graphic should be empty)
- Thick lines only (5px+ minimum), large icons (200px+ minimum)
- ONE SINGLE MESSAGE per graphic (not 3 related messages)

⚠️ BEFORE GENERATING: Review your prompt and count elements

• If your description has 5+ items → STOP. Split into multiple graphics
• If your workflow has 5+ stages → STOP. Show only 3-4 high-level steps
• If your comparison has 4+ methods → STOP. Show only top 3 or Our vs Best Baseline

Content limits per graphic type (STRICT): | Graphic Type | Max Elements | Max Words | Reject If | Good Example | |--------------|--------------|-----------|-----------|--------------| | Flowchart | 3-4 boxes MAX | 8 words | 5+ stages, nested steps | "DISCOVER → VALIDATE → APPROVE" (3 words) | | Key findings | 3 items MAX | 9 words | 4+ metrics, paragraphs | "95% ACCURATE" "2X FASTER" "FDA READY" (6 words) | | Comparison chart | 3 bars MAX | 6 words | 4+ methods, legend text | "OURS: 95%" "BEST: 85%" (4 words) | | Case study | 1 case, 3 elements | 6 words | Multiple cases, substories | Logo + "18 MONTHS" + "to discovery" (2 words) | | Timeline | 3-4 points MAX | 8 words | Year-by-year detail | "2020 START" "2022 TRIAL" "2024 APPROVED" (6 words) |

Example - WRONG (7-stage workflow - TOO COMPLEX):

# ❌ BAD - This creates tiny unreadable text like the drug discovery poster
python scripts/generate_schematic.py "Drug discovery workflow showing: Stage 1 Target Identification, Stage 2 Molecular Synthesis, Stage 3 Virtual Screening, Stage 4 AI Lead Optimization, Stage 5 Clinical Trial Design, Stage 6 FDA Approval. Include success metrics, timelines, and validation steps for each stage." -o figures/workflow.png
# Result: 7+ stages with tiny text, unreadable from 6 feet - POSTER FAILURE

Example - CORRECT (simplified to 3 key stages):

# ✅ GOOD - Same content, split into ONE simple high-level graphic
python scripts/generate_schematic.py "POSTER FORMAT for A0. ULTRA-SIMPLE 3-box workflow: 'DISCOVER' → 'VALIDATE' → 'APPROVE'. Each word in GIANT bold (120pt+). Thick arrows (10px). 60% white space. NO substeps, NO details. 3 words total. Readable from 10 feet." -o figures/workflow_overview.png
# Result: Clean, impactful, readable - can add detail graphics separately if needed

Example - WRONG (complex case studies with multiple sections):

# ❌ BAD - Creates cramped unreadable sections
python scripts/generate_schematic.py "Case studies: Insilico Medicine (drug candidate, discovery time, clinical trials), Recursion Pharma (platform, methodology, results), Exscientia (drug candidates, FDA status, timeline). Include company logos, metrics, and outcomes." -o figures/cases.png
# Result: 3 case studies with 4+ elements each = 12+ total elements, tiny text

Example - CORRECT (one case study, one key metric):

# ✅ GOOD - Show ONE case with ONE key number
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONE case study card: Company logo (large), '18 MONTHS' in GIANT text (150pt), 'to discovery' below (60pt). 3 elements total: logo + number + caption. 50% white space. Readable from 10 feet." -o figures/case_single.png
# Result: Clear, readable, impactful. Make 3 separate graphics if you need 3 cases.

Example - WRONG (key findings too complex):

# BAD - too many items, too much detail
python scripts/generate_schematic.py "Key findings showing 8 metrics: accuracy 95%, precision 92%, recall 94%, F1 0.93, AUC 0.97, training time 2.3 hours, inference 50ms, model size 145MB with comparison to 5 baseline methods" -o figures/findings.png
# Result: Cramped graphic with tiny numbers

Example - CORRECT (key findings simple):

# GOOD - only 3 key items, giant numbers
python scripts/generate_schematic.py "POSTER FORMAT for A0. KEY FINDINGS with ONLY 3 large cards. Card 1: '95%' in GIANT text (120pt) with 'ACCURACY' below (48pt). Card 2: '2X' in GIANT text with 'FASTER' below. Card 3: checkmark icon with 'VALIDATED' in large text. 50% white space. High contrast colors. NO other text or details." -o figures/findings.png
# Result: Bold, readable impact statement

Font size reference for poster prompts: | Element | Minimum Size | Prompt Keywords | |---------|--------------|-----------------| | Main numbers/metrics | 72pt+ | "huge", "very large", "giant", "poster-size" | | Section titles | 60pt+ | "large bold", "prominent" | | Labels/captions | 36pt+ | "readable from 6 feet", "clear labels" | | Body text | 24pt+ | "poster-readable", "large text" |

Always include in prompts:

• "POSTER FORMAT" or "for A0 poster" or "readable from 6 feet"
• "VERY LARGE TEXT" or "huge bold fonts"
• Specific text that should appear (so it's baked into the image)
• "minimal text, maximum impact"
• "high contrast" for readability
• "generous margins" and "no text near edges"

---

CRITICAL: AI-Generated Graphic Sizing

⚠️ Each AI-generated graphic should focus on ONE concept with MINIMAL content.

Problem: Generating complex diagrams with many elements leads to small text.

Solution: Generate SIMPLE graphics with FEW elements and LARGE text.

Example - WRONG (too complex, text will be small):

# BAD - too many elements in one graphic
python scripts/generate_schematic.py "Complete ML pipeline showing data collection, 
preprocessing with 5 steps, feature engineering with 8 techniques, model training 
with hyperparameter tuning, validation with cross-validation, and deployment with 
monitoring. Include all labels and descriptions." -o figures/pipeline.png

Example - CORRECT (simple, focused, large text):

# GOOD - split into multiple simple graphics with large text

# Graphic 1: High-level overview (3-4 elements max)
python scripts/generate_schematic.py "POSTER FORMAT for A0: Simple 4-step pipeline. 
Four large boxes: DATA → PROCESS → MODEL → RESULTS. 
GIANT labels (80pt+), thick arrows, lots of white space. 
Only 4 words total. Readable from 8 feet." -o figures/overview.png

# Graphic 2: Key result (1 metric highlighted)
python scripts/generate_schematic.py "POSTER FORMAT for A0: Single key metric display.
Giant '95%' text (150pt+) with 'ACCURACY' below (60pt+).
Checkmark icon. Minimal design, high contrast.
Readable from 10 feet." -o figures/accuracy.png

Rules for AI-generated poster graphics: | Rule | Limit | Reason | |------|-------|--------| | Elements per graphic | 3-5 maximum | More elements = smaller text | | Words per graphic | 10-15 maximum | Minimal text = larger fonts | | Flowchart steps | 4-5 maximum | Keeps labels readable | | Chart categories | 3-4 maximum | Prevents crowding | | Nested levels | 1-2 maximum | Avoids complexity |

Split complex content into multiple simple graphics:

Instead of 1 complex diagram with 12 elements:
→ Create 3 simple diagrams with 4 elements each
→ Each graphic can have LARGER text
→ Arrange in poster with clear visual flow

---

Step 0: MANDATORY Pre-Generation Review (DO THIS FIRST)

⚠️ BEFORE generating ANY graphics, review your content plan:

For EACH planned graphic, ask these questions:

1. Element count: Can I describe this in 3-4 items or less?

• ❌ NO → Simplify or split into multiple graphics
• ✅ YES → Continue

1. Complexity check: Is this a multi-stage workflow (5+ steps) or nested diagram?

• ❌ YES → Flatten to 3-4 high-level steps only
• ✅ NO → Continue

1. Word count: Can I describe all text in 10 words or less?

• ❌ NO → Cut text, use single-word labels
• ✅ YES → Continue

1. Message clarity: Does this graphic convey ONE clear message?

• ❌ NO → Split into multiple focused graphics
• ✅ YES → Continue to generation

Common patterns that ALWAYS fail (reject these):

• "Show stages 1 through 7..." → Split into high-level overview (3 stages) + detail graphics
• "Multiple case studies..." → One case per graphic
• "Timeline from 2015 to 2024 with annual milestones..." → Show only 3-4 key years
• "Comparison of 6 methods..." → Show only top 3 or Our method vs Best baseline
• "Architecture with all layers and connections..." → High-level only (3-4 components)

Step 1: Plan Your Poster Elements

After passing the pre-generation review, identify visual elements needed:

1. Title Block - Stylized title with institutional branding (optional - can be LaTeX text)
2. Introduction Graphic - Conceptual overview (3 elements max)
3. Methods Diagram - High-level workflow (3-4 steps max)
4. Results Figures - Key findings (3 metrics max per figure, may need 2-3 separate figures)
5. Conclusion Graphic - Summary visual (3 takeaways max)
6. Supplementary Icons - Simple icons, QR codes, logos (minimal)

Step 2: Generate Each Element (After Pre-Generation Review)

⚠️ CRITICAL: Review Step 0 checklist before proceeding.

Use the appropriate tool for each element type:

For Schematics and Diagrams (scientific-schematics):

# Create figures directory
mkdir -p figures

# Drug discovery workflow - HIGH-LEVEL ONLY, 3 stages
# BAD: "Stage 1: Target ID, Stage 2: Molecular Synthesis, Stage 3: Virtual Screening, Stage 4: AI Lead Opt..."
# GOOD: Collapse to 3 mega-stages
python scripts/generate_schematic.py "POSTER FORMAT for A0. ULTRA-SIMPLE 3-box workflow: 'DISCOVER' (120pt bold) → 'VALIDATE' (120pt bold) → 'APPROVE' (120pt bold). Thick arrows (10px). 60% white space. ONLY these 3 words. NO substeps. Readable from 12 feet." -o figures/workflow_simple.png

# System architecture - MAXIMUM 3 components
python scripts/generate_schematic.py "POSTER FORMAT for A0. ULTRA-SIMPLE 3-component stack: 'DATA' box (120pt) → 'AI MODEL' box (120pt) → 'PREDICTION' box (120pt). Thick vertical arrows. 60% white space. 3 words only. Readable from 12 feet." -o figures/architecture.png

# Timeline - ONLY 3 key milestones (not year-by-year)
# BAD: "2018, 2019, 2020, 2021, 2022, 2023, 2024 with events"
# GOOD: Only 3 breakthrough moments
python scripts/generate_schematic.py "POSTER FORMAT for A0. Timeline with ONLY 3 points: '2018' + icon, '2021' + icon, '2024' + icon. GIANT years (120pt). Large icons. 60% white space. NO connecting lines or details. Readable from 12 feet." -o figures/timeline.png

# Case study - ONE case, ONE key metric
# BAD: "3 case studies: Insilico (details), Recursion (details), Exscientia (details)"
# GOOD: ONE case with ONE number
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONE case study: Large logo + '18 MONTHS' (150pt bold) + 'to discovery' (60pt). 3 elements total. 60% white space. Readable from 12 feet." -o figures/case1.png

# If you need 3 cases → make 3 separate simple graphics (not one complex graphic)

For Stylized Blocks and Graphics (Nano Banana Pro):

# Title block - SIMPLE
python scripts/generate_schematic.py "POSTER FORMAT for A0. Title block: 'ML FOR DRUG DISCOVERY' in HUGE bold text (120pt+). Dark blue background. ONE subtle icon. NO other text. 40% white space. Readable from 15 feet." -o figures/title_block.png

# Introduction visual - SIMPLE, 3 elements only
python scripts/generate_schematic.py "POSTER FORMAT for A0. SIMPLE problem visual with ONLY 3 icons: drug icon, arrow, target icon. ONE label per icon (80pt+). 50% white space. NO detailed text. Readable from 8 feet." -o figures/intro_visual.png

# Conclusion/summary - ONLY 3 items, GIANT numbers
python scripts/generate_schematic.py "POSTER FORMAT for A0. KEY FINDINGS with EXACTLY 3 cards only. Card 1: '95%' (

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [LeonChaoX](https://github.com/LeonChaoX)
- **Source:** [LeonChaoX/qinyan-academic-skills](https://github.com/LeonChaoX/qinyan-academic-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.